Gender: Female 
Age: 35-44
Ethnicity: White / Caucasian 
Marital status: Married 
First language: English 
Religion: Christian 
Education: Graduate degree
Mother's education: Graduate degree
Father's education: Graduate degree
Self-assessed socioeconomic status (1-10): 8
Home: Own 
Employment: Employed full time
Occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer);Clerical support workers (those who perform clerical duties such as organizing, storing, computing and retrieving information)
Mother's occupation: Service and sales workers (whose who provide personal and protective services related to travel, housekeeping, catering, personal care, protection against fire and unlawful acts)
Father's occupation: Manager or business owner (those who plan, direct, coordinate and evaluate the overall activities of enterprises, governments and other organizations)
Hobbies: Use social media;Go to museums and galleries;Do arts and crafts;Go to gigs;Listen to classical music 
Devices used daily: Laptop;Tablet;Smartphone;Smartwatch 
NLP tools known: Spell checker (i.e., correcting misspellings);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Grammar checker (i.e., correcting grammar mistakes) 
NLP tools used: Spell checker (i.e., correcting misspellings);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Grammar checker (i.e., correcting grammar mistakes) 
NLP tools they would find useful: Spell checker (i.e., correcting misspellings);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Text-to-Speech (i.e, computers being able to read out loud written language);Grammar checker (i.e., correcting grammar mistakes);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants) 
Frequency of LLM use: Every day
LLMs used: Character.AI;ChatGPT;GitHub Copilot;Google Gemini;Snapchat My AI;Perplexity 
LLM use cases: Coding;Translation;Digital content creation (e.g., creation of social media content);Analyze data;Generate art 
Contexts of LLM use: Work;Personal;Entertainment;Learning 
Prompt 1: How do I write a Python script to analyze employee feedback and identify key themes
Prompt 2: Write a SQL query to calculate average employee productivity based on task completion data
Prompt 3: Create a JavaScript function to calculate bonuses based on performance scores
Prompt 4: How can a company like QuickComm manage major structural changes effectively
Prompt 5: How should a tech company implement wellness programs to ensure long-term success
Prompt 6: Could you write a script in R to visualize employee satisfaction survey results
Prompt 7: What’s the best way to collect feedback from employees during a company restructure
Prompt 8: How do team dynamics impact employee satisfaction
Prompt 9: Create a Python function to generate a random team assignment list for group projects
Prompt 10: How do I build a dashboard in Python to track employee engagement trends